Where Do Weather Forecasts Go Wrong?
How does a slow-moving disturbance like Hurricane Dorian so quickly transform into a terrifying Category 5 storm without forecasters noticing?
By the time NOAA updated its prediction Dorian had dropped 24 inches of rain on the Bahamas. The country’s prime minister called Hurricane Dorian a “historic tragedy.” To date, the official death toll from Hurricane Dorian in the Bahamas has risen to 61.
So how do we explain this extreme error in forecasting? Did the meteorologists entirely miss this storm? Was it hiding in plain sight? What exactly happened.
We’ll explain how weather forecasting can sometimes go amiss.
What Went Wrong With The Weather Forecast?
People have been trying to forecast the weather for a very long time. Aristotle devoted four volumes to meteorology; his student Theophrastus summed up ancient forecasting: “The popular saying about flies is true…when they bite excessively, it is a sign of rain.”
But while computing power and modeling have improved exponentially over the last few decades, the digital age’s promises of hyper-localized weather forecasts that can provide zip code level information seem to elude us.
Today’s forecasting suffers from three key problems:
First, weather forecasts in most parts of the world are still woefully inaccurate. Because of the resources and costs required to run current systems, good governmental infrastructure – from sensing to modeling to data dissemination – exists almost exclusively in developed countries. Yet the stakes are, if anything, higher in the developing world, where farming and infrastructure are more exposed and less resilient.
Second, even in places where the government is doing a great job, we still get generic answers to specific questions. The needs of the Spanish wind-energy industry differ greatly from the needs of organizations concerned with flood risk in Malaysia. Both industries require much more specialized forecasts than the generalized, regional weather trends that traditional forecasting provides. The forecasts generated by major national/international modeling centers aren’t equipped to solve the specific weather problems of individuals, groups, businesses or industries.
The result of this inadequate weather data is poor planning and disseminated economies that impact people, countries and entire regions.
Third, climate change has made it even more difficult to get weather forecasting right. With such phenomena as rapid onset rain and severe storms becoming more and more common the limits of the traditional way of gathering and analyzing weather are becoming glaringly evident. Climate change also exacerbates the problem mentioned above: lack of access to sufficiently specific forecasts in developing countries that need it most.
The Good News? It’s in the Cloud
Fortunately, Big Data-driven, AI-modeled solutions such as those developed by ClimaCell are removing the costly guesswork out of forecasting. Technological advances are making it possible to harness our entire connected world to produce hyper-accurate forecasts.
This innovative approach to getting a jump on the weather is taking place on the cloud, which means that prohibitively high labor, hardware, and deployment costs are a thing of the past. As a result, developing countries like the Bahamas can now rapidly deploy, update, and customize their own models and get much more accurate forecasts out to their citizens when they most need it.